A variety of methods have been proposed in the art to automatically detect cracks, uneven parts, etc. of a road surface or tunnel wall, which are heretofore detected visually. For instance Japanese Patent Application Nos. 229563/1983 and 233923/1954 have disclosed the following method: As shown in FIGS. 15 and 16, a laser scanning system comprising a laser oscillator 2, a mirror 3 and an electric motor 4, and a light receiving sensor 5, a distance recorder (not shown) are mounted on a vehicle 1. The road is scanned with the laser beam in a road crossing direction, and light scattered from the road is received by the light receiving sensor 5. When, in this case, there are no cracks or the like in the road surface to which the laser beam is applied, as shown in the part (a) of FIG. 17 a predetermined quantity of scattered light is received by the light receiving sensor 5. When, on the other hand, there are cracks or the like in the road surface, as shown in the part (b) of FIG. 7, the quantity of light received by the light receiving sensor 5 is greatly reduced because of a shadow effect. In addition, in the method proposed, the output of the light receiving sensor 5 together with the output of the distance recorder is recorded by a video tape recorder (VTR) or the like, and then stored in a special purpose image memory. The data stored in the image memory are as shown in FIG. 18: that is, the X-direction address represents a vehicle traveling direction, the Y-direction address, a road crossing direction, and the Z-direction address, data (multi-gradation or binary data) on cracks. That is, the magnitude, position and configuration of an uneven part such as a crack can be detected by analyzing the data thus stored.
On the other hand, the method of automatically recognizing cracks using such as a road surface image has demanded for provision of a technique of detecting a linear pattern such as a crack with high accuracy because of the following reasons:
(I) There are a lot of noises because a road includes aggregate etc. PA0 (II) Cracks are locally changed in direction and in width. PA0 (III) There are a number of uncontinuous parts, cracks occurring intermittently. PA0 (IV) The road surface condition depending greatly on the environmental conditions, construction method, etc. of the road, a variety of noises are superposed on one another. And formed crack patterns are not uniform; there may be formed lateral cracks, or honeycomb cracks.
Examples of a conventional linear pattern recognizing method are as follows:
(1) An image different in density is converted into binary data, to extract the region of dark (or light) lines, and thereafter a line thinning operation is carried out, to recognize the line.
(2) It is determined through correlation with a line detecting operator that there is a line in the high correlation region.
(3) The densities of picture elements in a plurality of directions of a point is summed up to obtain a line existing direction with respect to the point. This operation is repeatedly carried out in a follow-up mode, to recognize the line.
In addition, in the field of a character recognizing technique, the following methods are available:
(4) The black signal of a binary image is counted both in the X-direction and in the Y-direction, and a projection waveform is formed with the count values as waveform values, and collation is made with the reference pattern, to recognize the character.
(5) The region where lines are concentrated as in a character region is extracted from the result of projection of a wide region, and after each character is taken out of it, the projection waveform is collated with the reference pattern, to recognize the character.
The above-described conventional techniques (1), (2) and (3) are still disadvantageous in the following points: In general, they are weak against noise, and they are low in recognition accuracy although performing intricate operations. Hence, they are not applicable to the field of detecting cracks etc. of a road surface where a lot of noise components are generated, and the lines formed therein are intricate in configuration.
In general, an operation of projection has an effect that density is averaged in the direction of projection. Therefore, in the above-described conventional techniques (4) and (5) in which a wide region is projected, it is difficult to recognize a thin linear pattern, and even if the projection is made in the line existing direction, it is impossible to recognize an intricate line such as a crack. Those techniques suffer further from the difficulty that it is necessary to provide the reference pattern for recognition.
Accordingly, an object of this invention is to eliminate the above-described difficulties. More specifically, an object of the invention is to provide a linear pattern recognizing method which can detect an intricate linear pattern such as cracks in a road surface with high accuracy.
Another object of the invention is to provide a linear pattern recognizing method which can detect especially a branch of an intricate linear pattern such as cracks in a road surface.
A further object of the invention is to provide a linear pattern recognizing method which can accurately perform a line detecting operation even in the case of a directional image which is formed for instance by a flying spot method.